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/*
* Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "arm_compute/core/Types.h"
#include "utils/ImageLoader.h"
#include "utils/Utils.h"
using namespace arm_compute;
using namespace utils;
/** Gaussian 3x3 matrix
*/
const int16_t gaussian3x3[] =
{
1, 2, 1,
2, 4, 2,
1, 2, 1
};
/** Gaussian 5x5 matrix
*/
const int16_t gaussian5x5[] =
{
1, 4, 6, 4, 1,
4, 16, 24, 16, 4,
6, 24, 36, 24, 6,
4, 16, 24, 16, 4,
1, 4, 6, 4, 1
};
class NEONConvolutionExample : public Example
{
public:
bool do_setup(int argc, char **argv) override
{
/** [Accurate padding] **/
PPMLoader ppm;
if(argc < 2)
{
// Print help
std::cout << "Usage: ./build/neon_convolution [input_image.ppm]\n\n";
std::cout << "No input_image provided, creating a dummy 640x480 image\n";
// Initialize just the dimensions and format of your buffers:
src.allocator()->init(TensorInfo(640, 480, Format::U8));
}
else
{
ppm.open(argv[1]);
// Initialize just the dimensions and format of your buffers:
ppm.init_image(src, Format::U8);
}
// Initialize just the dimensions and format of the temporary and destination images:
tmp.allocator()->init(*src.info());
dst.allocator()->init(*src.info());
// Apply a Gaussian 3x3 filter to the source image followed by a Gaussian 5x5:
// The function will automatically update the padding information inside input and output to match its requirements
conv3x3.configure(&src, &tmp, gaussian3x3, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED);
conv5x5.configure(&tmp, &dst, gaussian5x5, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED);
// Now that the padding requirements are known we can allocate the images:
src.allocator()->allocate();
tmp.allocator()->allocate();
dst.allocator()->allocate();
// Fill the input image with the content of the PPM image if a filename was provided:
if(ppm.is_open())
{
ppm.fill_image(src);
output_filename = std::string(argv[1]) + "_out.ppm";
}
/** [Accurate padding] **/
return true;
}
void do_run() override
{
//Execute the functions:
conv3x3.run();
conv5x5.run();
}
void do_teardown() override
{
// Save the result to file:
if(!output_filename.empty())
{
save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM
}
}
private:
Image src{}, tmp{}, dst{};
NEConvolution3x3 conv3x3{};
NEConvolution5x5 conv5x5{};
std::string output_filename{};
};
/** Main program for convolution test
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Path to PPM image to process )
*/
int main(int argc, char **argv)
{
return utils::run_example<NEONConvolutionExample>(argc, argv);
}
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